The present invention relates to wireless telecommunications in general, and, more particularly, to a technique for modeling the propagation of wireless signals in buildings.
Indoor wireless telecommunications has been the subject of intense investigation in recent years for both voice and data communication. One particular area of investigation is how to ensure the adequate propagation of a wireless signal from a base station to a wireless terminal through a typical indoor environment. The walls, furniture and other objects in a typical indoor environment scatter the wireless signal and thus produce a complex multipath channel in which the signal propagation characteristics are substantially more difficult to predict than those in outdoor contexts.
Typically, the signal propagation characteristics are considered when designing and installing an indoor wireless telecommunications system. In particular, the signal propagation characteristics are advantageously considered when determining how many base stations are needed to provide coverage for a building and where in the building those base stations should be located. Because base stations are typically expensive to install and operate, it is advantageous to be able to determine how to provide the necessary coverage for the building with the fewest number of base stations. To do this, several techniques have been developed for modeling the propagation of wireless signals indoors.
One technique in the prior art for measuring and modeling indoor signal propagation is adapted from the power-law decay model used in modeling outdoor environments. The power-law decay model assumes that the base station""s antenna is high above the ground and that there is line-of-sight propagation to the wireless terminal. In this case, the mean power, P, received at the wireless terminal decays in inverse proportion to the square of the distance from the transmitter,                               P          ∝                      1                          r              2                                      ,                            (                  Eq          .                      xe2x80x83                    ⁢          1                )            
up to some break-point. Beyond that breakpoint, the mean power at the wireless terminal decays in inverse proportion to the fourth power of the distance from the transmitter:                     P        ∝                  1                      r            4                                              (                  Eq          .                      xe2x80x83                    ⁢          2                )            
The location of the break-point is determined by the location at which the ground bounce signal interferes with the line-of-sight signal. For indoor environments, Equation 1 has been adapted to Equation 3, where xcex3 is fit to empirical trial measurements of the building of interest, in well-known fashion:                     P        ∝                  1                      r            γ                                              (                  Eq          .                      xe2x80x83                    ⁢          3                )            
Unfortunately, Equation 3 typically does not provide a satisfactory model of the building of interest for aiding a designer in designing a wireless system for the building. The use of Equation 3 does, however, have advantages. First, the empirical data needed to fit xcex3 is easily obtainable, and second the fitting of xcex3 and subsequent use of Equation 3 as a model is computationally straightforward.
H. L. Bertoni, W. Honcharenko, L. R. Maciel, and H. H. Xia, xe2x80x9cUHF Propagation Prediction for Wireless Personal Communications,xe2x80x9d Proc. of the IEEE, vol. 82, pp. 1333-1359 (September 1994); and J. B. Anderson, T. S. Rappaport, and S. Yoshida, xe2x80x9cPropagation Measurements and Models for Wireless Communications Channels,xe2x80x9d IEEE Communic. Mag., pp. 42-49 (January 1995) both provide an excellent overview of wireless propagation models in the prior art.
Another technique in the prior art for measuring and modeling indoor RF propagation is adapted from ray-tracing techniques, as taught by S. J. Fortune et al., U.S. Pat. No. 5,450,615, issued Sep. 12, 1995. According to this technique the RF propagation within a building is predicted by modeling an RF signal as a plurality of rays that pass through objects in the building or are reflected off of objects in the building or both. An advantage of this technique is that the resulting wireless propagation model can be very effective for modeling the RF propagation characteristics of the building of interest. There are, however, two disadvantages of this technique. The first disadvantage is that the technique requires an extremely detailed floorplan of the building of interest including the major RF obstacles in the building. For a typical office building, this can be burdensome. The second disadvantage is that the technique is computationally intense, and, therefore, typically requires fast, expensive computers.
Therefore, there exists the need for a wireless propagation model that is effective, that does not require a great deal of empirical data about the building to be gathered and that can be reasonably implemented on a typical desktop computer.
Some embodiments of the present invention are capable of modeling the propagation of RF signals in an indoor environment without the restrictions and disadvantages of techniques in the prior art. In particular, some embodiments are extremely effective, yet require only a modest amount of architectural information and computational power.
These advantages may be found in some embodiments of the present invention that comprise six distinct phases. In one phase, the mean wall separation, {overscore (d)}, of one floor of a building is estimated. In a second phase, a reflection coefficient, s, is estimated for the floor in general. In a third phase, a number of trial RF propagation measurements are made to gather empirical data about the RF propagation characteristics of the floor. In the fourth phase, the mean wall separation, {overscore (d)}, the reflection coefficient, s, and the trial RF propagation measurements are fit, using well-known techniques, into a wireless propagation model, such as             P      ⁡              (        r        )              =                            P          0                          2          ⁢          π          ⁢                      xe2x80x83                    ⁢          D                    ⁢                                    π            ⁢                          xe2x80x83                        ⁢            ξ                                2            ⁢            r                              ⁢              ⅇ                              -            r                    /          ξ                      ,
where P(r) is the measured or predicted power at a distance, r, from the transmitter,   D  =            ξ      2        ⁡          [                                                                  (                                  s                                      d                    _                                                  )                            2                        +                                          (                                  1                  ξ                                )                            2                                      -                  xe2x80x83                ⁢                  s                      d            _                              ]      
and "xgr" is the parameter that is fit to the empirical data. In the fifth phase the wireless propagation model is used to predict the RF signal strength throughout the floor from a base station at a given location, and in the six phase one or more base stations are installed in the building based on the results predicted by the wireless propagation model. The fifth and sixth phases can be thereafter repeated for other building with sufficiently similar characteristics.